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Register a new userDeveloping an effective system for medical image classification using Group Sparsity and fuzzy enhancement
تطوير نظام فعال لتصنيف الصور الطبية باستخدام خلخلة المجموعة و التحسين الضبابي
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Aِl-Baath University ورقة بحثية
Publication date
2016
fields
Informatics Engineering
and research's language is
العربية
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Shamra Editor
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قمنا في هذا البحث بإدخال خوارزمية اختيار السمات المستندة على الضبط regularization للاستفادة من خصائص الخلخلة و تجميع السمات و ادراجه في مهمة تصنيف الصور الطبية، باستخدام الطريقة المعتمدة على خلخلة المجموعة group sparsity التي تُمكن من الإبقاء أو الحذف على مجموعة كاملة من السمات. إن الفكرة الأساسية في خلخلة المجموعة هي حذف السمات التي لا تؤثر على عملية الاستعادة بدلاً من الإبقاء على هذه السمات و اعطائها أوزان قليلة، و بالتالي تعتبر كخوارزمية لتحسين النظام عن طريق زيادة دقة النتائج بالإضافة الى تخفيض المتطلبات الزمنية و التخزينية التي يحتاجها النظام.
In this research we introduce a regularization based feature selection algorithm to benefit from sparsity and feature grouping properties and incorporate it into the medical image classification task. Using this group sparsity (GS) method, the whole group of features are either selected or removed. The basic idea in GS is to delete features that do not affect the retrieval process, instead of keeping them and giving these features small weights. Therefore, GS improves system by increasing accuracy of the results, plus reducing space and time requirements needed by the system.
References used
Lehmann, Thomas M., et al., et al. Automatic categorization of medical images for content-based retrieval and data mining. s.l. : Computerized Medical Imaging and Graphics, 2005
Kohnen, Michael, et al., et al. Quality of DICOM header information for image categorization. 2002
Zhang, Shaoting, et al., et al. Automatic Image Annotation and Retrieval Using Group Sparsity. s.l. : IEEE, 2012
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